Prediction of Gender-Biased Perceptions of Learners and Teachers Using Machine Learning

Ghazala Kausar, Sajid Saleem, Fazli Subhan, Mazliham Mohd Suud (), Mansoor Alam and M. Irfan Uddin
Additional contact information
Ghazala Kausar: Department of English, National University of Modern Languages, Islamabad 44000, Pakistan
Sajid Saleem: Department of Computer Science, National University of Modern Languages, Lalazar, Rawalpindi 46000, Pakistan
Fazli Subhan: Department of Computer Science, National University of Modern Languages, Islamabad 44000, Pakistan
Mazliham Mohd Suud: Faculty of Computer and Information, Multimedia University, Cyberjaya 63100, Malaysia
Mansoor Alam: Faculty of Computer and Information, Multimedia University, Cyberjaya 63100, Malaysia
M. Irfan Uddin: Institute of Computing, Kohat University of Science and Technology, Kohat 26000, Pakistan

Sustainability, 2023, vol. 15, issue 7, 1-18

Abstract: Computers have enabled diverse and precise data processing and analysis for decades. Researchers of humanities and social sciences are increasingly adopting computational tools such as artificial intelligence (AI) and machine learning (ML) to analyse human behaviour in society by identifying patterns within data. In this regard, this paper presents the modelling of teachers and students’ perceptions regarding gender bias in text books through AI. The data was collected from 470 respondents through a questionnaire using five different themes. The data was analysed with support vector machines (SVM), decision trees (DT), random forest (RF) and artificial neural networks (ANN). The experimental results show that the prediction of perceptions regarding gender varies according to the theme and leads to the different performances of the AI techniques. However, it is observed that when data from all the themes are combined, the best results are obtained. The experimental results show that ANN, on average, demonstrates the best performance by achieving an accuracy of 87.2%, followed by RF and SVM, which demonstrate an accuracy of 84% and 80%, respectively. This paper is significant in modelling human behaviour in society through AI, which is a significant contribution to the field.

Keywords: gender bias; text books; perceptions; artificial intelligence; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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